CS229: Machine Learning


Course Description   This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Course Information

Time and Location
Lectures: Mon, Wed 1:30 PM - 2:50 PM (PT) at NVIDIA Auditorium
Quick Links
Contact and Communication
Ed is the primary method of communication for this class. Please do NOT reach out to the instructors (or course staff) directly, otherwise your questions may get lost. Due to a large number of inquiries, we encourage you to first read the "Course Logistics and FAQ" quick link above for commonly asked questions, and then create a post on Ed to contact the course staff.
This quarter we will be using Ed as the course forum.
  • All official announcements and communication will happen over Ed.
  • Any questions regarding course content and course organization should be posted on Ed. You are strongly encouraged to answer other students' questions when you know the answer.
  • If there are private matters specific to you (e.g. special accommodations, requesting alternative arrangements etc.), please create a private post on Ed.
  • For longer discussions with TAs, please attend office hours.
  • TA office hours can be found on Canvas. For the course calendar, see also Canvas and the "Syllabus and Course Materials" quick link above.
  • Before the beginning of the course, please contact the course coordinator for logistical questions (ideally after consulting the FAQ link).

Course Staff

To help with project advice, each member of course staff's ML expertise is also listed below.

Course Manager
Head Course Assistant
Zhoujie Ding
Course Assistants
Samir Agarwala
CV, Scene Understanding, Graphics
Sonia Chu
Rishi Desai
CV, Ensemble Models, LLMs
Kefan Dong
RL Theory, ML Theory, LLMs
Jacob Frausto
CV, Robotics, RL
Hong Jun Jeon
Info Theory, ML Theory, RL
Priya Khandelwal
Multimodal Learning, LLMs, MLSys
Hermann Kumbong
John So
Robotics, RL, CV
Alex Wang
Time Series, AI+Healthcare
Zedian Xiao
Shijia Yang
3D Vision, Multimodal Learning
Eric Zhang
CV, NLP, Finance